Environment-Specific Route-Library Adaptation for Decentralized Multi-Robot Navigation via Hybrid RRT and Behavior Cloning in Grid-Based Industrial Environments

Decentralized multi-robot navigation in grid-based industrial environments must reach goals and avoid collisions without centralized control or direct communication. We study a hybrid framework pairing an offline Rapidly-Exploring Random Tree (RRT) expert with a Behavior Cloning (BC) local policy and route reuse, evaluated in a reproducible simulator; collisions are predicted blocked-move events, not physical contacts. Our central result is a quantitative analysis of environment-specific route-library adaptation: a route library generated for one map and deployed on another leaves robots blocked by unfamiliar obstacles, whereas regenerating it on the deployment map cuts collisions by 38–85% and task failures by 43–70% across two- to ten-robot fleets, and replicates on a third corridor layout (30 seeds; Mann–Whitney p<10−5; permutation pperm<0.001). Simpler remedies, filtering or repairing invalid routes, recover most of this gain, and a non-learning scripted connector matches the trained policy: the effect lives in the route library itself. A centralized prioritized-planning baseline bounds all communication-free variants from above; capping the online RRT baseline’s planning budget preserves its per-task collision quality but sharply raises task failures. A collision-history-sharing add-on coordinating through a shared map rather than messaging gives a limited, layout-dependent benefit not surviving multiple-comparison correction.